一种改进的稀疏迭代最近点算法

稀疏迭代最近点算法是针对含有噪声点的点云配准提出的,但它却存在对目标点云中的离群点敏感、运行效率低等问题。针对这些问题,基于邻域信息的对应点对寻找方法提出了一种改进的稀疏迭代最近点算法。改进的稀疏迭代最近点算法首先使用改进的基于PCA的点云初始配准调整两片点云的位置,而后使用基于邻域信息的对应点对寻找方法为精配准寻找对应点对,针对对应点对,使用乘法器的交替方向法(ADMM)求得最优的变换矩阵。实验表明,对含离群点的斯坦福兔子、盆栽等点云来说,改进后的算法能够处理目标点云含有离群点的情况,并且算法的配准速度平均提高了30%。     

An orientation reliability matrix for the iterative closest point algorithm

This paper proposes a matrix which represents the reliability of the rotation components of the iterative closest point (ICP) algorithm in range image registration. We show that the reliability of the ICP algorithm depends on the surface normal vectors of the object.     

An algorithm for tracking multiple targets

An algorithm for tracking multiple targets in a cluttered enviroment is developed. The algorithm is capable of initiating tracks, accounting for false or missing reports, and processing sets of dependent reports. As each measurement is received, probabilities are calculated for the hypotheses that the measurement came from previously known targets in a target file, or from a new target, or that the measurement is false. Target states are estimated from each such data-association hypothesis using a Kalman filter. As more measurements are received, the probabilities of joint hypotheses are calculated recursively using all available information such as density of unknown targets, density of false targets, probability of detection, and location uncertainty. This branching technique allows correlation of a measurement with its source based on subsequent, as well as previous, data. To keep the number of hypotheses reasonable, unlikely hypotheses are eliminated and hypotheses with similar target estimates are combined. To minimize computational requirements, the entire set of targets and measurements is divided into clusters that are solved independently. In an illustrative example of aircraft tracking, the algorithm successfully tracks targets over a wide range of conditions.     

低视点下遮挡自适应感知的多目标跟踪算法

目的 针对低视点多目标跟踪场景的遮挡问题,提出一种能够遮挡自适应感知的多目标跟踪算法。方法 首先根据每帧图像的全局遮挡状态,提出了“自适应抗遮挡特征”,增强目标特征对遮挡的感知和调整能力。同时,采用“级联筛查机制”,减少由遮挡带来的目标特征剧烈变化而认定为“虚新入目标”的错误跟踪现象。最后,考虑到历史模板库中存在遮挡的模板对跟踪性能的影响,根据每一帧中目标的局部遮挡状态,提出自适应干扰模板更新机制,进一步提高对遮挡的应变和适应能力。结果 实验结果表明,本文算法在MOTA(multiple object tracking accuracy)、M OTP (multiple object tracking precision)、FN(false negatives)、Rcll (recall)、ML (mostly lost tracklets)等指标上明显优于STAM(spatial-temporal attention mechanism)、ATAF(aggregate tracklet appearance features)、STRN (spatial-temporal relat...     

Closed-world tracking of multiple interacting targets for indoor-sports applications

In this paper we present an efficient algorithm for tracking multiple players during indoor sports matches. A sports match can be considered as a semi-controlled environment for which a set of closed-world assumptions regarding the visual as well as the dynamical properties of the players and the court can be derived. These assumptions are then used in the context of particle filtering to arrive at a computationally fast, closed-world, multi-player tracker. The proposed tracker is based on multiple, single-player trackers, which are combined using a closed-world assumption about the interactions among players. With regard to the visual properties, the robustness of the tracker is achieved by deriving a novel sports-domain-specific likelihood function and employing a novel background-elimination scheme. The restrictions on the player’s dynamics are enforced by employing a novel form of local smoothing. This smoothing renders the tracking more robust and reduces the computational complexity of the tracker. We evaluated the proposed closed-world, multi-player tracker on a challenging data set. In comparison with several similar trackers that did not utilize all of the closed-world assumptions, the proposed tracker produced better estimates of position and prediction as well as reducing the number of failures.     

三维高速机动目标跟踪交互式多模型算法

对三维机动目标,尤其是高速机动目标的跟踪一直是目标跟踪领域的重点和难点,通过二维或解耦模型扩展并不一定能满足精度要求.提出了一种基于常速模型、"当前"统计模型、带约束的三维常速率协同转弯模型的交互式多模型算法.通过对包括匀加速模型、Singer模型、"当前"统计模型在内的不同模型组合进行Monte-Carlo仿真比较表明本算法对三维高速机动目标跟踪是有效性,并具有很好的实用性.     

An evidence‐based iterative content trust algorithm for the credibility of online news

People encounter more information than they can possibly use every day. But all information is not necessarily of equal value. In many cases, certain information appears to be better, or more trustworthy, than other information. And the challenge that most people then face is to judge which information is more credible. In this paper we propose a new problem called Corroboration Trust, which studies how to find credible news events by seeking more than one source to verify information on a given topic. We design an evidence‐based corroboration trust algorithm called TrustNewsFinder, which utilizes the relationships between news articles and related evidence information (person, location, time and keywords about the news). A news article is trustworthy if it provides many pieces of trustworthy evidence, and a piece of evidence is likely to be true if it is provided by many trustworthy news articles. Our experiments show that TrustNewsFinder successfully finds true events among conflicting information and identifies trustworthy news better than the popular search engines. Copyright © 2009 John Wiley & Sons, Ltd.     

几种典型的微弱点状多运动目标跟踪算法对比研究

重点研究了序列图像情况下几种典型的微弱点状多运动目标实时跟踪算法,虽然它们都能够完成不同背景环境下目标的全程跟踪,但跟踪性能存在较大的差异,PDA算法具有较高的实时性,但容易出现目标的偏移和聚合现象;JPDA算法理论上解决了多目标数据关联问题,但跟踪过程存在较大误差且由于计算量大难以在工程中应用;基于最大熵高斯聚类算法对模糊隶属度进行了修正,数据关联性高且有效避免了目标的误跟和丢失现象。通过对几种典型算法的仿真分析,为多目标跟踪算法的优化提供可靠依据。     

高速高机动目标自适应IMM跟踪算法

针对目标运动过程中有转弯机动等复杂运动模式的高速高机动目标,设计了自适应两层IMM跟踪算法。该算法内层由改进的机动目标当前统计模型构成,把目标速度方向角作为伪测量值进行滤波,实时获得目标的角速度和角加速度;外层模型由常速模型和曲线模型构成,把内层模型得到的切向加速度和转弯角速度作为曲线模型参数,利用IMM算法进行滤波。仿真结果表明,该算法对高速高机动目标具有较高的跟踪精度,算法实现简单,具有一定的实际应用价值。     

An efficient visual tracking method for multiple moving targets

An efficient algorithm of the edge detection according to integrating the edge gradient with the average filter is proposed, which can significantly reduce sensitivity of the background subtraction method to noise and illumination. Taking into account the features of the target such as color, size, etc., a new modified Nearest Neighbor （NN） algorithm for data association using the target features is designed. A designed Interacting Multiple Model （IMM） filter is utilized to track the maneuvering target motion, i.e. the feature point （called the centroid of the target） motion of the target. The algorithms are validated via an example with natural video sequences. The results show the algorithms are performances and validity for visual tracking. In complex environment, the algorithm can still work well.     

各向异性带宽自适应水面运动目标跟踪算法

传统Mean-Shift跟踪算法缺少核函数带宽更新策略,故无法解决无人艇跟踪的水面运动目标轮廓变化各向异性问题,提出一种各向异性带宽自适应的Mean-Shift跟踪算法。先用黎曼积分将特征子模型概率密度的归一化常数C_h近似为积分形式,从而获得不同尺度参数h对应的C_h间关系式。然后用梯度上升法使目标模型和目标候选模型之间的相似度函数达到局部最大,由此估计目标在下一帧的带宽与位置。最后为防止带宽更新时结果过小或过大,引入两个正则化参数修正尺度参数。实验结果表明,所提算法对外形轮廓非同比变化的水面运动目标跟踪具有各向异性的带宽自适应调节能力,型心位置准确率较传统Mean-Shift和各向同性带宽自适应Mean-Shift提高了约77.2%和31.1%,运行速度可达20.7 fps,显示了其鲁棒性和实时性。     

针对机动目标的改进型交互多模型跟踪算法研究

为了提高对机动目标的跟踪精度,更准确地获得目标实时位置与速度信息,提出了一种改进型交互多模型跟踪算法.采用目标特征数据为初始数据提供限定域,然后在滤波器中加入调节参数,从而利用目标状态增益矩阵与协方差矩阵的迭代完成对跟踪精度的优化.实验仿真分析了机动目标的3种常见状态,并与传统交互多模型跟踪算法进行了对比.实验结果显示...     

一种改进的运动目标跟踪与轨迹记录算法

针对目前运动目标的跟踪与记录方法占用存储空间较大的缺点,提出了一种减少存储空间的记录算法,即先用三帧差分算法和Snake算法相结合检出运动物体的轮廓,再利用Hausdorff算法对提出的轮廓进行匹配,并将匹配后的轮廓和运动轨迹以文本文件存储,大大降低了运动目标轨迹记录存储容量.实际运用表明,改进后的记录存储空间相当于通常视频文件的万分之一.该算法适于长时间记录运动目标轨迹.     

A particle swarm optimisation algorithm with interactive swarms for tracking multiple targets

We propose a novel particle swarm optimisation algorithm that uses a set of interactive swarms to track multiple pedestrians in a crowd. The proposed method improves the standard particle swarm optimisation algorithm with a dynamic social interaction model that enhances the interaction among swarms. In addition, we integrate constraints provided by temporal continuity and strength of person detections in the framework. This allows particle swarm optimisation to be able to track multiple moving targets in a complex scene. Experimental results demonstrate that the proposed method robustly tracks multiple targets despite the complex interactions among targets that lead to several occlusions.     

一种机器人未知环境下动态目标跟踪交互多模滤波算法

为了解决机器人同时定位、地图构建和目标跟踪问题,提出了一种基于交互多模滤波(interacting multiple model filter, IMM)的方法.该方法将机器人状态、目标状态和环境特征状态作为整体来构成系统状态向量并利用全关联扩展式卡尔曼滤波算法对系统状态进行估计,由此随着迭代估计的进行,系统各对象状态之间将产生足够的相关性,这种相关性能够正确反映各对象状态估计间的依赖关系,因此提高了目标跟踪的准确性.该方法进一步和传统的IMM滤波算法相结合,从而解决了目标运动模式未知性问题,IMM方法的采用使系统在完成目标追踪的同时还能对其运动模态进行估计,进而提高了该算法对于机动目标的跟踪能力.仿真实验验证了该方法对机器人和目标的运动轨迹以及目标运动模态进行估计的准确性和有效性.     

模糊聚类粒子滤波的点状交叉多目标跟踪算法

提出了一种新的低信噪比红外序列图像多目标检测跟踪算法,该算法有机地结合了TBD检测算法与模糊聚类粒子滤波跟踪算法。首先通过多帧TBD处理后,检测出运动目标的初始位置、运动速度,然后在跟踪阶段采用粒子滤波算法估计目标运动状态,并在估计位置开一个跟踪窗进行检测、模糊聚类概率融合。对真实红外图像序列进行实验仿真,仿真结果验证了该算法具有良好的实时性与很高的精确性。     

An iterative algorithm for maximizing robust performance in loop‐shaping design

In this paper, an algorithm that gives the best achievable performance bound on a given control problem is proposed using the loop‐shaping design framework. In view of standard design requirements, the robust performance is maximized at low and high frequencies while keeping the robust stability margin above a specified level, and the robust stability margin is directly improved at mid frequencies (around crossover). The proposed frequency‐dependent optimization problem is cast in an LMI framework. The resulting solution algorithm simultaneously synthesizes loop‐shaping weights and a stabilizing controller that achieve the maximum performance for a given level of robust stability margin corresponding to sufficient gain and phase margins of the closed‐loop system. Copyright © 2012 John Wiley & Sons, Ltd.     

Adaptive iterative learning control for coordination of second‐order multi‐agent systems

In this paper, an efficient framework is proposed to the consensus and formation control of distributed multi‐agent systems with second‐order dynamics and unknown time‐varying parameters, by means of an adaptive iterative learning control approach. Under the assumption that the acceleration of the leader is unknown to any follower agents, a new adaptive auxiliary control and the distributed adaptive iterative learning protocols are designed. Then, all follower agents track the leader uniformly on [0,T] for consensus problem and keep the desired distance from the leader and achieve velocity consensus uniformly on [0,T] for the formation problem, respectively. The distributed multi‐agent coordinations performance is analyzed based on the Lyapunov stability theory. Finally, simulation examples are given to illustrate the effectiveness of the proposed protocols in this paper.Copyright © 2013 John Wiley & Sons, Ltd.     

一种基于改进UPF的运动声阵列交互多模型目标跟踪方法

针对运动声阵列在有色噪声环境中的非线性滤波跟踪实时性问题,提出一种基于改进粒子滤波的交互式多模型目标跟踪(IMM-IUPF)方法.该方法采用最小斜度单形无迹变换结合变尺度无迹变换的 Sigma点选取策略,对标准无迹粒子滤波(UPF)进行改进,大幅缩减UPF样本选取计算量;将改进的UPF与交互式多模型(IMM)相结合,通过定义自适应增益修正系数,弥补样本点缩减造成的精度降低问题;最终通过与传统的基于IMM的粒子滤波(IMM-PF)和基于IMM的无迹粒子滤波(IMM-UPF)进行Matlab数值仿真对比验证所提出算法的实用性.     

An interacting multi-pattern probabilistic data association(IMP-PDA) algorithm for target tracking

A theoretical development of a novel approach for target tracking based on multiple patterns extracted from measurement sequences is presented in this paper. The introduction of patterns leads to a new paradigm for developing high performance algorithms. An interacting multi-pattern probabilistic data association (IMP-PDA) algorithm is developed, taking the advantage of clever formulation of the interacting multiple model approach. The IMP-PDA algorithm employs distance, directional and maneuver information for data association, which enhances significantly the capability of discriminating correct measurements from false measurements